Emergency rescue training system based on big data scheduling

Abstract

The invention relates to the technical field of emergency rescue, and discloses an emergency rescue training system based on big data scheduling, which comprises a data acquisition module, a big data processing and analyzing device, a scheduling module, a virtual reality training platform and a real-time feedback mechanism, the data acquisition module specifically comprises a GPS positioning unit, a video monitoring unit, a sensor data unit and a social media information unit. A real rescue scene is simulated through an immersive technology, the training effect is improved, the psychological load of rescue workers is accurately evaluated through a psychological state analysis model, a training scheme is dynamically adjusted, psychological toughness is enhanced, and the actual combat coping capacity is ensured; an intelligent voice recognition module is arranged in the system, multilingual instructions are transcribed in real time and rapidly analyzed and converted into operation, cross-language communication efficiency is improved, seamless information connection is ensured, the multi-country cooperation ability is enhanced, the overall cooperative combat ability of a rescue team is improved, and it is guaranteed that rescue actions are efficiently and orderly carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency rescue, and specifically to an emergency rescue training system based on big data scheduling. Background Art

[0002] The so-called emergency rescue refers to an action system that quickly organizes and coordinates resources for efficient rescue in emergencies. The key lies in the rapid transmission of information and the rational allocation of resources. The emergency rescue training system is a comprehensive platform integrating education, simulation, practice, and evaluation functions, aiming to improve the professional skills, reaction speed, and teamwork ability of emergency rescue personnel. This system usually combines modern technological means, such as virtual reality (VR), augmented reality (AR), simulation software, etc., to provide an emergency rescue personnel with a training environment close to the real scene.

[0003] With the complexity and variability of modern society, the need for emergency rescue is becoming increasingly urgent. The traditional training methods can no longer meet the effective and accurate rescue needs. There is an urgent need for a training system that uses big data technology for scheduling and optimization to improve the actual combat ability and response efficiency of rescue personnel and achieve the rational allocation and efficient utilization of training resources. Summary of the Invention

[0004] The purpose of the present invention is to provide an emergency rescue training system based on big data scheduling to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: An emergency rescue training system based on big data scheduling, including a data collection module, a big data processing and analyzer, a scheduling module, a virtual reality training platform, and a real-time feedback mechanism. The data collection module specifically includes a GPS positioning unit, a video monitoring unit, a sensor data unit, and a social media information unit. The GPS positioning unit is used to track the location of rescue personnel in real time. The video monitoring unit captures the on-site dynamics. The sensor data unit monitors environmental parameters. The social media information unit collects public opinion information. The big data processing and analyzer uses machine learning algorithms to fuse and analyze multi-source data, construct a dynamic model, predict potential risks, and optimize the scheduling strategy.

[0006] The scheduling module intelligently allocates rescue resources according to the analysis results to ensure a rapid response. Among them, the scheduling module includes a resource database, a task allocation algorithm, and a real-time communication interface. The resource database stores various rescue resource information. The task allocation algorithm dynamically matches resources with tasks. The real-time communication interface ensures that instructions are conveyed immediately to improve the cooperation efficiency. The task allocation algorithm uses the following formula:

[0007] T(z)=∑(k=1to p)ck*dk(z)

[0008] Among them, \(c_k\) is the resource weight, \(d_k(z)\) is the resource matching degree, and \(p\) is the total number of resources. By dynamically adjusting \(c_k\), optimal resource allocation is achieved to ensure effective and accurate rescue.

[0009] The virtual reality training platform adopts immersive technology to restore real rescue scenes. Trainers conduct practical drills through VR devices to experience the pressure of the real environment.

[0010] The real-time feedback mechanism generates an evaluation report based on the drill performance, points out deficiencies and provides improvement suggestions to help improve the actual combat ability. Among them, the evaluation report includes operation scoring, psychological state analysis, and coping strategy optimization. The operation scoring details the performance of each link, the psychological state analysis reveals the stress coping ability, and the coping strategy optimization provides targeted improvement plans, comprehensively improving the comprehensive quality and emergency response ability of rescue personnel, and ensuring that the data of each drill is synchronously updated to the cloud database for subsequent analysis and strategy adjustment.

[0011] Preferably, the GPS positioning unit includes multiple high-precision positioning chips, a dynamic trajectory recording module, and a real-time transmission interface. The multiple high-precision positioning chips work together to ensure accurate positioning. The dynamic trajectory recording module records the moving trajectory in real time, and the real-time transmission interface immediately uploads the position information to the big data processing and analyzer.

[0012] Preferably, the video monitoring unit is equipped with a high-definition camera and an image recognition algorithm. The high-definition camera captures subtle pictures, and the image recognition algorithm analyzes in real time to extract key information to assist in decision-making. The image recognition algorithm uses the following formula:

[0013] \(f(x)=\sum_{i = 1}^{n}w_ig_i(x)\)

[0014] Among them, \(w_i\) is the weight of each feature, \(g_i(x)\) is the feature function, and \(n\) is the total number of features. By optimizing the weight \(w_i\), the recognition accuracy is improved.

[0015] Preferably, the sensor data unit integrates multiple sensors, including a temperature sensor, a humidity sensor, and a gas sensor, to monitor environmental changes in real time to ensure comprehensive data.

[0016] The social media information unit obtains real-time public opinion through web crawler technology, analyzes the sentiment tendency, identifies key events, and combines with the big data processing and analyzer to generate a comprehensive situation map to assist in command decision-making and improve the efficiency of emergency rescue.

[0017] Preferably, among them, the immersive technology includes three-dimensional modeling, a physics engine, and real-time rendering. The three-dimensional modeling constructs a realistic scene, the physics engine simulates real physical interactions, and the real-time rendering ensures smooth pictures and enhances the immersion.

[0018] Preferably, the psychological state analysis adopts a psychological stress index model, combines multi-dimensional physiological data such as heart rate and respiratory rate, accurately evaluates the psychological load, dynamically tracks emotional changes, and provides a scientific basis for personalized psychological intervention. The psychological state analysis includes the following formula:

[0019] P(y) = α * Hr + β * Rr + γ * St

[0020] Among them, α, β, and γ are weight coefficients, Hr is the heart rate, Rr is the respiratory rate, and St is the skin conductivity. By dynamically adjusting the weight coefficients, the model can reflect the psychological state of the rescue personnel in real time, effectively improve the pertinence and effectiveness of psychological intervention, ensure that the rescue personnel maintain the best state in a high-pressure environment, and further optimize the model parameters.

[0021] Preferably, the system also includes an intelligent voice recognition module that real-time transcribes voice commands, combines natural language processing technology, quickly analyzes the command content, and converts it into specific operation commands to improve the command efficiency. The intelligent voice recognition module continuously optimizes the recognition accuracy through deep learning algorithms, reduces the misrecognition rate, and ensures the accurate transmission of commands; the module has built-in multi-language support, covering multiple languages such as English, Japanese, and French, adapts to different international rescue scenarios, improves cross-language communication efficiency, ensures seamless information docking, enhances multi-national cooperation capabilities, and the module also has an offline recognition function to ensure the normal execution of commands in a network-free environment.

[0022] The present invention provides an emergency rescue training system based on big data scheduling. It has the following beneficial effects:

[0023] (1) By setting multi-dimensional data collection and analysis, the present invention realizes all-round situation awareness, improves the emergency response speed, and ensures scientific decision-making; the system dynamically updates the situation map through real-time data synchronization, accurately locates the rescue needs, optimizes the resource allocation, and improves the rescue efficiency.

[0024] (2) By using immersive technology to simulate real rescue scenarios, the present invention improves the training effect. Through the psychological state analysis model, it accurately evaluates the psychological load of rescue personnel, dynamically adjusts the training plan, enhances psychological resilience, and ensures the actual combat response ability; the system has a built-in intelligent voice recognition module that real-time transcribes multi-language commands, quickly analyzes and converts them into operations, improves cross-language communication efficiency, ensures seamless information docking, enhances multi-national cooperation capabilities, improves the overall coordinated combat ability of the rescue team, and ensures the efficient and orderly progress of rescue operations.

[0025] (3) Through big data scheduling, the present invention realizes the optimal allocation of resources, reduces the response time, and improves the rescue success rate. The addition of image recognition algorithms, task allocation algorithms, and the formula for psychological state analysis further improves the intelligent level of the system, ensures accurate and effective decision-making, comprehensively improves the actual combat ability of rescue, and guarantees life safety. Brief Description of the Drawings

[0026] Figure 1 It is a schematic diagram of the overall system architecture of the present invention;

[0027] Figure 2 It is a view of the data acquisition module of the present invention;

[0028] Figure 3 It is a view of the scheduling module of the present invention. Detailed Embodiments

[0029] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0030] Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present invention and should not be construed as a limitation of the present invention.

[0031] A preferred embodiment of an emergency rescue training system based on big data scheduling provided by the present invention is as Figures 1-3 shown: An emergency rescue training system based on big data scheduling includes a data acquisition module, a big data processing and analyzer, a scheduling module, a virtual reality training platform, and a real-time feedback mechanism. The multi-source information is collected through the data acquisition module, the in-depth mining is carried out by the big data processing and analyzer, the real scene is simulated by the virtual reality training platform, and the training effect is evaluated by the real-time feedback mechanism. The data acquisition module specifically includes a GPS positioning unit, a video monitoring unit, a sensor data unit, and a social media information unit. The GPS positioning unit is used to track the location of rescue personnel in real time, the video monitoring unit captures the on-site dynamics, the sensor data unit monitors the environmental parameters, and the social media information unit collects public opinion information;

[0032] Among them, the GPS positioning unit includes a plurality of high-precision positioning chips, a dynamic trajectory recording module, and a real-time transmission interface. The plurality of high-precision positioning chips work together to ensure accurate positioning. The dynamic trajectory recording module records the moving trajectory in real time, and the real-time transmission interface immediately uploads the location information to the big data processing and analyzer;

[0033] Among them, the video monitoring unit is equipped with a high-definition camera and an image recognition algorithm. The high-definition camera captures fine pictures, and the image recognition algorithm analyzes in real time to extract key information to assist in decision-making; The image recognition algorithm adopts the following formula:

[0034] f(x) = ∑(i = 1 to n) wi * gi(x)

[0035] Wherein, wi are the weights of each feature, gi(x) is the feature function, and n is the total number of features. By optimizing the weights wi, the recognition accuracy is improved;

[0036] Wherein, the sensor data unit integrates multiple sensors, including a temperature sensor, a humidity sensor, and a gas sensor, to monitor environmental changes in real time and ensure comprehensive data;

[0037] Wherein, the social media information unit obtains real-time public opinion through web crawler technology, analyzes the sentiment tendency, identifies key events, combines with the big data processing and analyzer to generate a comprehensive situation map, assists in command and decision-making, and improves the efficiency of emergency rescue;

[0038] The big data processing and analyzer uses machine learning algorithms to perform fusion analysis on multi-source data, constructs a dynamic model, predicts potential risks, and optimizes the scheduling strategy;

[0039] The scheduling module intelligently allocates rescue resources according to the analysis results to ensure rapid response. Among them, the scheduling module includes a resource database, a task allocation algorithm, and a real-time communication interface. The resource database stores information on various rescue resources. The task allocation algorithm dynamically matches resources with tasks, and the real-time communication interface ensures instant transmission of instructions to improve the coordination efficiency; The task allocation algorithm uses the following formula:

[0040] T(z) = ∑(k = 1 to p) ck * dk(z)

[0041] Wherein, ck are the resource weights, dk(z) is the resource matching degree, and p is the total number of resources. By dynamically adjusting ck, optimal resource allocation is achieved to ensure effective and accurate rescue.

[0042] The virtual reality training platform adopts immersive technology to restore real rescue scenes. Trainers conduct practical drills through VR devices to experience the pressure of the real environment. Among them, the immersive technology includes 3D modeling, a physics engine, and real-time rendering. The 3D modeling constructs a realistic scene, the physics engine simulates real physical interactions, and the real-time rendering ensures smooth images to enhance the immersion;

[0043] The real-time feedback mechanism generates an evaluation report based on the exercise performance, points out deficiencies and provides improvement suggestions to help enhance the actual combat ability. Among them, the evaluation report includes operation scoring, psychological state analysis and coping strategy optimization. The operation scoring details the performance of each link, the psychological state analysis reveals the stress coping ability, and the coping strategy optimization provides targeted improvement plans, comprehensively improving the comprehensive quality and emergency response ability of rescue personnel. Ensure that the data of each exercise is synchronously updated to the cloud database for subsequent analysis and strategy adjustment; the psychological state analysis uses a psychological stress index model, combines multi-dimensional physiological data such as heart rate and breathing rate to accurately evaluate the psychological load, dynamically tracks emotional changes, and provides a scientific basis for personalized psychological intervention. The psychological state analysis includes the following formula:

[0044] P(y) = α * Hr + β * Rr + γ * St

[0045] Where α, β, and γ are weight coefficients, Hr is the heart rate, Rr is the breathing rate, and St is the skin conductance. By dynamically adjusting the weight coefficients, the model can reflect the psychological state of rescue personnel in real time, effectively improving the pertinence and effectiveness of psychological intervention, ensuring that rescue personnel maintain the best state in a high-pressure environment, and further optimizing the model parameters;

[0046] The system also includes an intelligent voice recognition module that transcribes voice commands in real time, combines natural language processing technology to quickly analyze the command content, and converts it into specific operation commands to improve the command efficiency. The intelligent voice recognition module continuously optimizes the recognition accuracy through deep learning algorithms, reduces the misrecognition rate, and ensures the accurate transmission of commands; the module has built-in multi-language support, covering multiple languages such as English, Japanese, and French, adapting to different international rescue scenarios, improving cross-language communication efficiency, ensuring seamless information docking, and enhancing multi-national cooperation capabilities. The module also has an offline recognition function to ensure the normal execution of commands in a network-free environment.

[0047] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "including", "containing" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent in such process, method, article or device.

[0048] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An emergency rescue training system based on big data scheduling, including a data acquisition module, a big data processing and analyzer, a scheduling module, a virtual reality training platform and a real-time feedback mechanism, characterized in that: The data acquisition module specifically includes a GPS positioning unit, a video monitoring unit, a sensor data unit and a social media information unit. The GPS positioning unit is used to track the location of rescuers in real time, the video monitoring unit captures on-site dynamics, the sensor data unit monitors environmental parameters, and the social media information unit collects public opinion information; the big data processing and analyzer uses a machine learning algorithm to perform fusion analysis on multi-source data, build a dynamic model, predict potential risks, and optimize scheduling strategies; The scheduling module intelligently allocates rescue resources according to the analysis results to ensure rapid response. The scheduling module includes a resource database, a task allocation algorithm and a real-time communication interface. The resource database stores various types of rescue resource information, the task allocation algorithm dynamically matches resources and tasks, and the real-time communication interface ensures that instructions are transmitted in real time to improve coordination efficiency. The task allocation algorithm adopts the following formula: T(z)=∑(k=1to p)ck*dk(z) Among them, ck is the resource weight, dk(z) is the resource matching degree, and p is the total number of resources. By dynamically adjusting ck, optimal resource allocation is achieved to ensure effective and accurate rescue. The virtual reality training platform adopts immersive technology to restore real rescue scenes. Trainees can conduct practical exercises through VR equipment to feel the pressure of real environment. The real-time feedback mechanism generates an evaluation report based on the drill performance, points out deficiencies and provides improvement suggestions to help improve actual combat capabilities. The evaluation report includes operation scores, psychological state analysis and response strategy optimization. The operation scores refine the performance of each link, the psychological state analysis reveals the ability to cope with stress, and the response strategy optimization provides targeted improvement plans to comprehensively improve the comprehensive quality and emergency response capabilities of rescue personnel, and ensure that the data of each drill is synchronously updated to the cloud database to facilitate subsequent analysis and strategy adjustments.

2. The emergency rescue training system based on big data scheduling according to claim 1 is characterized by: The GPS positioning unit includes multiple high-precision positioning chips, a dynamic trajectory recording module and a real-time transmission interface. The multiple high-precision positioning chips work together to ensure accurate positioning. The dynamic trajectory recording module records the movement trajectory in real time, and the real-time transmission interface uploads the location information to the big data processing and analyzer in real time.

3. The emergency rescue training system based on big data scheduling according to claim 1 is characterized in that: The video surveillance unit is equipped with a high-definition camera and an image recognition algorithm. The high-definition camera captures subtle images, and the image recognition algorithm performs real-time analysis, extracts key information, and assists in decision-making. The image recognition algorithm uses the following formula: f(x)=∑(i=1 to n)wi*gi(x) Among them, wi is the weight of each feature, gi(x) is the feature function, and n is the total number of features. By optimizing the weight wi, the recognition accuracy can be improved.

4. The emergency rescue training system based on big data scheduling according to claim 1 is characterized in that: The sensor data unit integrates multiple sensors, including temperature sensors, humidity sensors and gas sensors, to monitor environmental changes in real time and ensure comprehensive data; The social media information unit obtains real-time public opinion through crawler technology, analyzes emotional tendencies, identifies key events, and combines big data processing and analyzers to generate a comprehensive situation map to assist command decision-making and improve emergency rescue efficiency.

5. The emergency rescue training system based on big data scheduling according to claim 1 is characterized in that: in, Immersive technology includes 3D modeling, physics engine and real-time rendering. 3D modeling constructs realistic scenes, the physics engine simulates real physical interactions, and real-time rendering ensures smooth images and enhances the sense of immersion.

6. The emergency rescue training system based on big data scheduling according to claim 1 is characterized by: This psychological state analysis uses a psychological stress index model, combined with multi-dimensional physiological data such as heart rate and respiratory rate, to accurately assess psychological load, dynamically track emotional changes, and provide a scientific basis for personalized psychological intervention. This psychological state analysis includes the following formulas: P(y)=α*Hr+β*Rr+γ*St Among them, α, β, and γ are weight coefficients, Hr is heart rate, Rr is respiratory rate, and St is skin conductivity. By dynamically adjusting the weight coefficients, the model can reflect the psychological state of rescuers in real time, effectively improve the pertinence and effectiveness of psychological intervention, ensure that rescuers maintain the best state under high-pressure environments, and further optimize the model parameters.

7. The emergency rescue training system based on big data scheduling according to claim 1 is characterized by: The system also includes an intelligent voice recognition module, which transcribes voice commands in real time and combines natural language processing technology to quickly parse the command content and convert it into specific operational instructions to improve command efficiency. The intelligent voice recognition module continuously optimizes recognition accuracy through deep learning algorithms, reduces the misrecognition rate, and ensures that commands are accurately conveyed. The module has built-in multi-language support, covering English, Japanese, French and other languages, to adapt to different international rescue scenarios, improve cross-language communication efficiency, ensure seamless information connection, and enhance multi-national collaboration capabilities. The module also has offline recognition function to ensure normal execution of commands in a networkless environment.

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